Update app.py

app.py

@@ -83,12 +83,12 @@ orpheus_tts_model.to(tts_device)
 orpheus_tts_tokenizer = AutoTokenizer.from_pretrained(tts_model_name)
 print(f"Orpheus TTS model loaded to {tts_device}")
 
-# Some global parameters for chat responses
+# Global parameters for chat responses
 MAX_MAX_NEW_TOKENS = 2048
 DEFAULT_MAX_NEW_TOKENS = 1024
 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
 
-# (Image generation related code has been fully removed.)
+# (Image generation code has been removed.)
 
 MAX_SEED = np.iinfo(np.int32).max
 
@@ -200,7 +200,7 @@ def generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new
     if not text.strip():
         return None
     try:
-        # Removed in-function progress calls to maintain UI consistency.
+        # Generate speech without internal progress calls (UI progress is handled externally)
         input_ids, attention_mask = process_prompt(text, voice, orpheus_tts_tokenizer, tts_device)
         with torch.no_grad():
             generated_ids = orpheus_tts_model.generate(
@@ -233,7 +233,7 @@ def generate(
     repetition_penalty: float = 1.2,
 ):
     """
-    Generates chatbot responses with support for multimodal input, video processing,
+    Generates chatbot responses with support for video processing,
     TTS, and LLM-augmented TTS.
     
     Trigger commands:
@@ -335,64 +335,39 @@ def generate(
             yield gr.Audio(audio_output, autoplay=True)
             return
 
-    # Default branch for regular chat (text and multimodal without TTS).
+    # Default branch for regular chat (text without explicit TTS trigger)
     conversation = clean_chat_history(chat_history)
     conversation.append({"role": "user", "content": text})
-    # If files are provided, only non-image files (e.g. video) are processed via Qwen2VL.
-    if files:
-        # Process files using the processor (this branch no longer handles image generation)
-        if len(files) > 1:
-            inputs_list = [load_image(image) for image in files]
-        elif len(files) == 1:
-            inputs_list = [load_image(files[0])]
-        else:
-            inputs_list = []
-        messages = [{
-            "role": "user",
-            "content": [
-                *[{"type": "image", "image": img} for img in inputs_list],
-                {"type": "text", "text": text},
-            ]
-        }]
-        prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-        inputs = processor(text=[prompt_full], images=inputs_list, return_tensors="pt", padding=True).to("cuda")
-        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
-        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
-        thread.start()
-        buffer = ""
-        yield progress_bar_html("Processing with Qwen2VL")
-        for new_text in streamer:
-            buffer += new_text.replace("<|im_end|>", "")
-            time.sleep(0.01)
-            yield buffer
-    else:
-        input_ids = hermes_llm_tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
-        if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
-            input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
-            gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
-        input_ids = input_ids.to(hermes_llm_model.device)
-        streamer = TextIteratorStreamer(hermes_llm_tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {
-            "input_ids": input_ids,
-            "streamer": streamer,
-            "max_new_tokens": max_new_tokens,
-            "do_sample": True,
-            "top_p": top_p,
-            "top_k": top_k,
-            "temperature": temperature,
-            "num_beams": 1,
-            "repetition_penalty": repetition_penalty,
-        }
-        t = Thread(target=hermes_llm_model.generate, kwargs=generation_kwargs)
-        t.start()
-        outputs = []
-        yield progress_bar_html("Processing with DeepHermes LLM")
-        for new_text in streamer:
-            outputs.append(new_text)
-            yield "".join(outputs)
-        final_response = "".join(outputs)
-        yield final_response
+    # Process using the DeepHermes LLM
+    input_ids = hermes_llm_tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(hermes_llm_model.device)
+    streamer = TextIteratorStreamer(hermes_llm_tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {
+        "input_ids": input_ids,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "do_sample": True,
+        "top_p": top_p,
+        "top_k": top_k,
+        "temperature": temperature,
+        "num_beams": 1,
+        "repetition_penalty": repetition_penalty,
+    }
+    t = Thread(target=hermes_llm_model.generate, kwargs=generation_kwargs)
+    t.start()
+    outputs = []
+    yield progress_bar_html("Processing with DeepHermes LLM")
+    for new_text in streamer:
+        outputs.append(new_text)
+        yield "".join(outputs)
+    final_response = "".join(outputs)
+    yield final_response
+    # Also convert the final response to speech using a default voice ("tara")
+    audio_output = generate_speech(final_response, "tara", temperature, top_p, repetition_penalty, max_new_tokens)
+    yield gr.Audio(audio_output, autoplay=True)
 
 # Gradio Interface
 demo = gr.ChatInterface(
@@ -411,7 +386,6 @@ demo = gr.ChatInterface(
         ["@josh-llm What causes rainbows to form?"],
         ["@dan-tts Yo, I’m Dan, [groan] and yes, I can even sound annoyed if I have to."],
         ["Write python program for array rotation"],
-        [{"text": "summarize the letter", "files": ["examples/1.png"]}],
         ["@tara-tts Hey there, my name is Tara, [laugh] and I’m a speech generation model that can sound just like you!"],
         ["@tara-llm Who is Nikola Tesla, and why did he die?"],
         ["@emma-llm Explain the causes of rainbows"],